SAMSI Wraps up 2018 Fall Semester

Ernest Fokoué, a m associate professor of statistics at the Rochester Institute of Technology presents a mini-course at the 2018 Modern Math Workshop in Oct. 10, 2018. He presented an “Overview of Data Science: Regression and Classification” during the two-day workshop.

As 2018 closed, SAMSI reflects on a very productive fall semester.

Since opening programs in PMED and MUMS, SAMSI continues to bring together some of the best minds in applied mathematics and statistics to address modern issues across a broad spectrum of subjects. The institute has either hosted or co-sponsored five different events this past fall that discussed topics such as machine learning, climate, computer-based statistical modeling, using statistics in precision medicine and developing mathematical algorithms to identify gerrymandering in state voter maps.

2018 Modern Math Workshop
SAMSI was a major sponsor for the 2018 Modern Math Workshop (MMW) this past October in San Antonio, TX. The two-day workshop, held annually, focused on encouraging undergraduates, graduate students and recent PhDs from underrepresented minority groups to pursue careers in the mathematical sciences and help them to build mentoring networks.

Lisa Lebovici, a master’s student in statistics at Duke University, presents findings on gerrymandering data from a case involving the state of Maryland, during the Quantitative Redistricting Workshop held at Duke Oct. 8-9, 2018. Valuable information about the elections was presented at this workshop from social and political scientists, lawyers and mathematicians and statisticians.

Education & Outreach and Research Workshops
SAMSI also hosted a workshop for nearly 40 undergraduates in October that supported this year’s PMED program. Students were treated to talks on how statistics and applied mathematics are used in precision medicine. The students were also treated to a panel consisting of postdoctoral and graduate researchers who discussed their journey in academics and how they are preparing for future careers in the mathematical sciences.

October also included a workshop at Duke University called Quantitative Redistricting, that studied how to use statistical data, census research, sociology and computer-based algorithms to identify “gerrymandering,” an issue that affects the election process in states across the nation. Gerrymandering is the practice of manipulating the boundaries of (an electoral constituency) so as to favor one party or class.

The workshop helped to raise awareness of this important issue. The workshop, led by Jonathan Mattingly, Chair of the Department of Mathematics at Duke University, brought together professionals in sociology, political science, law and statistics and computer science to address this issue.

R and Spark are computing environments used in statistical science to extract data, compartmentalize it and help to develop algorithms to conduct research with the data provided.

Harner provided instruction to a class of more than 35 students and researchers combined. The instruction helped them to understand how to work within the environment and apply what they had learned to further their own personal research.

The workshop, which took place in Asheville, NC, was a national, interdisciplinary scientific research discussion on statistical modeling and climate science.

All of these events help achieve SAMSI’s vision to conduct programs that help to connect young researchers from academia, industry, national laboratories and government to address a wide variety of research using applied math and statistical and computer sciences.

Whether working with other entities or hosting events by themselves, SAMSI continues their dedication to serving the science and math community.

Bruce Pitman, a professor of mathematics from the University of Buffalo and a visiting researcher at SAMSI, opens up the MUMS workshop at Gross Hall, on the campus of Duke University in Aug. 2018. Pitman was one of many researchers attending the workshop that focused on the utilization of model uncertainty and uncertainty quantification methods to solve complex problems.